Recently, the dataset used for the contest was made open to the public via their website. volume2, Articlenumber:150055 (2015) The number of records in the datasets
Telecom Italia Big Data Challenge - SlideShare The results of the proposed networks are then validated using the Telecom Italia Dataset. This dataset provides information regarding the directional interaction strength between the Province of Trento different areas based on the calls exchanged between Telecom Italia Mobile users.The directional interaction strength between the area A and the area B is proportional to the number of calls issued from the area A to the area B.the spatial aggregation is the Trentino GRID squares and the Italian provinces.the temporeal aggregation values are in timeslots of ten minutes. Bogomolov, A. et al. & Ratti, C. Towards a comparative science of cities: using mobile traffic records in new york, london, and hong kong. Hence, the number of records si(t) in a grid square i at time t is computed as follows: where Rv,j(t) is the number of records in the coverage area v at time t, Av is the surface of the coverage area v and Avi is the surface of the spatial intersection between v and the square i. The unit of measurement (UOM) of the value recorded by the given sensor is specified in the Legend dataset. These data was used during the Big Data Challenge 2014, an online call for developers, researchers and designers from all over the world to come up with brand-new big data services and applications. Telecom Italia, in association with EIT ICT Labs, MIT Media Lab, Milan Polytechnic and Trento RISE, has launched the Big Data Challenge, an online call for developers, researchers and designers from all over the world to come up with brand-new big data services and applications. The data are accessible from the Harvard Dataverse repository but also from a public API provided by Dandelion (http://dandelion.eu) which is the original platform where the data were published for the Big Data Challenge. On the real-world Telecom Italia dataset, simulation results demonstrate the effectiveness of our proposal through prediction performance measure, spatial pattern comparison and statistical distribution verification. Gonzalez, M., Hidalgo, C. & Barabasi, A. Aleix Bassolas, Hugo Barbosa-Filho, Jos J. Ramasco, Hugo Barbosa, Surendra Hazarie, Gourab Ghoshal, Jan Priesmann, Lars Nolting, Aaron Praktiknjo, Carmen Cabrera-Arnau, Chen Zhong, Soong Moon Kang, Scientific Data publicly available is the dataset published by Telecom Italia in 2014 as "the Big Data Challenge" [5].
There are 18 telecommunications datasets available on data.world. The latter number is proportional to the number of calls generated from the Milan/Trentino square to the province, while the former is proportional to the number of calls from the province to the Milan/Trentino square. The simplest way to define hotspots is by choosing a threshold equal to the average of the city's activity, and considering hotspots as all the points with a density larger than that. GitHub - dwhitena/analyze-visualize-model-telecom-italia: Python (pandas, statsmodels, etc.) The dataset describes precipitation intensity over the province of Trento.the spatial aggregation is the Trentino GRID squares.The temporal values are provided every ten minutes. Two types of CDR datasets were also produced to measure the interaction intensity between different locations: one from a particular area (Trentino/Milan) to any of the Italian provinces and one quantifying the interactions within the city/province (e.g., Milan to Milan). 4 SETlayers. Different types of software and tools were used in the dataset generation process and it would have been too complicated to share and explain all the used source code used. Bajardi, P., Delfino, M., Panisson, A., Petri, G. & Tizzoni, M. Unveiling patterns of international communities in a global city using mobile phone data. 7), the selected areas show very different behavioural patterns. Some of the datasets referring to the Trentino territory are spatially aggregated using a grid. Harvard Dataverse https://doi.org/10.7910/DVN/9IZALB (2015), SpazioDati Harvard Dataverse https://doi.org/10.7910/DVN/5H0NUI (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/DVN/9Z6CKW (2015), MeteoTrentino Harvard Dataverse https://doi.org/10.7910/DVN/UPODNL (2015), MeteoTrentino Harvard Dataverse https://doi.org/10.7910/DVN/0RZVTA (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/DVN/S2UGMD (2015), SET, Telecom Italia Harvard Dataverse https://doi.org/10.7910/DVN/AMKZXM (2015), Citynews Harvard Dataverse https://doi.org/10.7910/DVN/NYQ23N (2015), Citynews Harvard Dataverse https://doi.org/10.7910/DVN/QWOE1R (2015), SpazioDati Harvard Dataverse https://doi.org/10.7910/DVN/KNMIVZ (2015). Since the text of the news articles is not provided, a service like diffbot (http://www.diffbot.com) or any other similar service (e.g., Apache Tika) could be used to extract the text from a given url. The Telecommunication activity dataset for the city of Milan (i.e., data citation 5 in the paper), which contains mobile network traffic. ADS A.P. Since the datasets come from various companies which have adopted different standards, their spatial distribution irregularity is aggregated in a grid with square cells. The data used in this paper is obtained from the dataset provided by Telecom Italia [], a large European telephone service provider, which mainly includes the communication records of telephone services, SMS services, and Internet activities (62 days, 500 million records) in Milan and Trentino. We would like to show you a description here but the site won't allow us. First Online: 20 June 2020 Part of the Contributions to Statistics book series (CONTRIB.STAT.) Song, C., Qu, Z., Blumm, N. & Barabasi, A. 2.1 Activity The activity data set consists of records with square id, time interval, sms-in activity, sms-out activity, call-in activity, call-out activity, internet . Consequently, researchers can study cities through the lens of hotspots' stability; the spatial structure of hotspots and their aforementioned categories can be studied to determine the typology of a city (e.g., mono-centric cities).
Telecom Data | Kaggle PLoS Computational Biology 10, 1003716 (2014).
Metropolitan Cellular Traffic Prediction Using Deep Learning Techniques This helps researchers to observe and understand the spatial distribution of the various datasets. In addition to the data described in this paper, the second edition also provides private mobility data (trips performed by customers of some car security and insurance companies), demographic data from Telecom Italia (e.g., gender, age-range and living area) and detailed Italian companies' information (e.g., number of employees, size and locations). Instead, news stories exhibit a strong weekly seasonality which is probably due to work cycles, since Saturdays and Sundays less news are published (on the website) respectively to other days. Thus, the area of Milan is composed of a grid overlay of 1,000 (squares with size of about 235235meters and Trentino is composed of a grid overlay of 6,575 squares (see Fig. 1). As you can see, the data was supplied in batch mode, using downloadable compressed files, or through API, if this kind of access is meaningful.API data access allows a specific audience to use data more quickly, easily and efficiently when they are looking to do something specific with the information. This dataset [Data citations 8,9] provides the directional interaction strengths between different areas of Milan and the Province of Trento. It can also be useful to visualize the data and the distribution of the events inside the geographical areas. Unfortunately, since it was not possible to share the input (raw) files, this code can not be executed to perfectly reproduce the datasets. Cross-checking different sources of mobility information. The Orange Telecom's Churn Dataset, which consists of cleaned customer activity data (features), along with a churn label specifying whether a customer canceled the subscription, will be used to develop predictive models. We select the following areas: Bocconi, one of the most famous Universities in Milan (Square id: 4259); Navigli district, one of the most famous nightlife places in Milan (Square id: 4456); Duomo, the city centre of Milan (Square id: 5060); Duomo, the city centre of Trento (Square id: 5200); Mesiano, the department of Engineering of the University of Trento (Square id: 5085); Bosco della citt, a forest near Trento (Square id: 4703). In the Telecommunications and Social pulse datasets, we provided record level data which are not algorithmically aggregated on purpose. For the latter, each task is performed for predicting service-specific traffic data based on a fully connected network. The dataset describes precipitation intensity over the province of Trento. and now .
Aujasvi-Moudgil/Forecasting-Mobile-Network-Traffic - GitHub This dataset provides, for specific instances, the total current flowing through the lines. Proceedings of the 9th Python in Science Conference 445, 5156 (2010). Tizzoni, M. et al. A.C. processed the dataset. The Telecom Italia Big Data Challenge now is Open Data Home Highlights At the beginning of 2014, Telecom Italia, in collaboration with several international partners, launched the Telecom Italia Big Data Challenge. This dataset provides information about the current administrative regions in Europe. ), others on a weekly basis (e.g., watching the favourite football team at the stadium). Cartography and Geographic Information Science 41, 260271 (2014). The precipitation datasets provide information about precipitation intensity and type over the geographical area. These metrics were also linked to socio-economical data in order to estimate poverty levels in a region. Error <600m. The weather data describe meteorological phenomena type and intensity in Milan and Trentino. 6. Date in the format YYYY-MM-DD HH24 : MI; Value: the ampere value of the current passing through a given powerline (Line id) at a given Timestamp.
dwhitena/analyze-visualize-model-telecom-italia - GitHub Moreover, there is also a weekly seasonality due to the work cycles behaviour of people (e.g., working days versus weekends). The scaling of human interactions with city size. ADS In order to spatially aggregate the CDRs inside the grid, each interaction is associated with the coverage area v of the RBS which handled it. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. G.T. Csji, B. et al. CAS This means that the whole community can benefit from both of our work on the database.The ODbL requires you to attribute your use of this data. This dataset contains all the articles published on the website trentotoday.it from 01/11/2013 to 31/12/2013. designed the dataset, processed the data and wrote the paper. It uses around 180 primary distribution lines (medium voltage lines) to bring energy from the national grid to Trentino's consumers. Since they adopt different standards, we organized two sections to describe them. The multi-source nature of the current dataset permits the modeling of multiple dimensions of a given geographical area and to address a variety of problems and scientific issues that range from the classic human mobility and traffic analysis studies to energy consumption and linguistic studies.
Spatial-Temporal Attention-Convolution Network for Citywide Cellular Telecom Italia made a dataset of its own mobile phone data (millions of anonymized and geo-referenced records of calls from Milan and . This dataset contains data derived from an analysis of geolocalized tweets originated from Milan during the months of November and December.Each row corresponds to a tweet. The sensors can measure different meteorological phenomena: Wind Direction, Wind Speed, Temperature, Relative Humidity, Precipitation, Global Radiation, Atmospheric Pressure and Net Radiation. Telecom Italia's board of directors has agreed to the spin-off of its 23 data centers into a separate business. The plot confirms our expectations. Hence, it is possible to capture the evolution observing permanent hotspots (places that are important all day), intermittent (with a lifespan of only few hours per day) and intermediate (with a lifespan ~ 12h). The dataset supplies information regarding the current flowing through the distribution lines and details about how the distribution lines are spread over the Trentino territory. The company is now looking for external investors for the new venture when it begins operations in 2021.
Algorithms | Free Full-Text | Citywide Cellular Traffic Prediction Google Scholar. Google Scholar. Limits of predictability in human mobility. Science 338, 267270 (2012). Douglass, R., Meyer, D., Ram, M., Rideout, D. & Song, D. High resolution population estimates from telecommunications data. The network was deployed in Milan and the dataset is provided by Telecom Italia. The Internet traffic is initiated from the nation identified by the Country code; Country code: the phone country code of the nation.
Cell Traffic Prediction Based on Convolutional Neural Network for In the precipitation layer colors go from blue (minimum mean intensity of precipitations) to red (the maximum one). Each state in Europe is composed by administrative regions that may be divided into sub-regions. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. & Capra, L. Poverty on the cheap. This means that (at most) 34% of population's data is collected, due to Telecom Italia's market share (http://www.agcom.it/documents/10179/1734740/Studio-Ricerca+24-07-2014/5541e017-3c7a-42ff-b82f-66b460175f68?version=1.0, date of access 06/08/2014). arXiv preprint arXiv: 1407.4885 (2014). The end interval time can be obtained by adding 600,000milliseconds (10min) to this value; SMS-in activity: activity proportional to the amount of received SMSs inside a given Square id and during a given Time interval. S This dataset provides information about the pollution intensity and type for Milan city. There is also code to generate the box-plots in this paper; Box-plots showing the calls, SMS, and Internet CDRs distributions per weekday and per cell in Milan. For instance, given the article http://www.milanotoday.it/eventi/concerti/eventi-capodanno-2014-milano.html, text: Tutti invitati al gran concerto di Capodanno in piazza [], title: Concerto Capodanno in piazza Duomo:, url: http://www.milanotoday.it/eventi/concerti/eventi-capodanno-2014-milano.html.
Information | Free Full-Text | Call Details Record Analysis: A - MDPI & Krings, G. A survey of results on mobile phone datasets analysis. The SMSs are sent from the nation identified by the Country code; SMS-out activity: activity proportional to the amount of sent SMSs inside a given Square id during a given Time interval.
Open Data Institute - node Trento The Telecom Italia Big Data Challenge dataset is unique in that, since it is a rich, open multi-source aggregation of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino (see Table 1 and Fig. Then, a new CDR is created recording the time of the interaction and the RBS which handled it. Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. Each record (or feature) describes a square providing the following information, This dataset provides information about the telecommunication activity over the Province of Trento.The dataset is the result of a computation over the Call Detail Records (CDRs) generated by the Telecom Italia cellular network over the city of Milano. November 12, 2020. The possible values are, - 0: the geometry comes directly from the original source, and has not been edited by SpazioDati or anyone, - 1: the geometry has been inferred by SpazioDati from other fields, such as the locality/municipality, - 2: the geometry has been geocoded from an address, geomComplex.accuracy: quality of the geometry. Moreover, Bocconi has less mobile phone activity than Duomo, which is the centre of the city and the most important tourist attraction. The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. MathSciNet SKIL Semantics & Knowledge Innovation Lab, We breathe bigdata, we think in graphs, we speak semantics. The Precipitation dataset [Data citations 14,15] contains values about the type and the intensity of the precipitation. de Montjoye, Y., Smoreda, Z., Trinquart, R., Ziemlicki, C. & Blondel, V. D4d-senegal: The second mobile phone data for development challenge. Article
Mobile phone activity in a city | Kaggle 15.
Telecom Italia's Big Data Challenge - Data Collaboratives The reason is that our goal is to give researchers the possibility both to extract known metrics and to design new ones. The lack of open datasets limits the number of potential studies and creates issues in the process of validation and reproducibility needed by the scientific community. The level of interaction between an area A of the Province of Trento and a province B is given as a pair of decimal numbers. (t) follows the rule: where k is a constant defined by Telecom Italia, which hides the true number of calls, SMS and connections. All articles published by the on-line newspaper Milano Today and Trento Today from 01/11/2013 and 31/12/2013 are contained in this dataset [Data citations 17,18]. Geo-located twitter as proxy for global mobility patterns. Data collectors divide Milan into 100100 regions, and all traffic data statistics are based on regions. The dataset (http://www.istat.it/it/archivio/104317, date of access 09/09/2015), released in Italian, is composed of four parts: Territorial Bases (Basi Territoriali), Administrative Boundaries (Confini Amministrativi), Census Variables (Variabili Censuarie) and data about Toponymy (Dati Toponomastici). The first set contains the geographical shapefile data of all the Italian regional areas. A plain language summary of the ODbL is available on the Open Data Commons website. The third contains census variables, divided into eight different groups: residential population, foreign population, families, education level, work status, commuting, accommodations info and building composition. wrote the paper. Barlacchi, G., De Nadai, M., Larcher, R. et al. We refer to this grid as the Trentino Grid. Internet a CDR is generated each time a user starts an Internet connection or ends an Internet connection. Quercia, D., Ellis, J., Capra, L. & Crowcroft, J. Tracking gross community happiness from tweets.
A multi-source dataset of urban life in the city of Milan and the These datasets are now freely available for anyone to use. Defined as type 2; Heavy: precipitation quantity equal to in [10,100] mm/h. The Grid dataset for the city of Milan (i.e., data citation 2 in the paper), which describes the tessellation of space into the areas over which such information is aggregated FBK takes is the scientific partner on big data and open data policy. ), which require different amount of electricity. Each sensor has a unique ID, a type and a location. processed the dataset. The challenge was organized by Telecom Italia, in association with EIT ICT Labs, SpazioDati, MIT Media Lab, Polytechnic University of Milan, Fondazione Bruno Kessler,University of Trento and TrentoRISE.The data provided in the dataset of the Big Data Challenge is geo-referenced (areas: Milan and the Autonomous Province of Trento Italy) and anonymized. SKILTelecom Italia, Trento, 38123, Italy, Gianni Barlacchi,Roberto Larcher,Antonio Casella,Cristiana Chitic,Giovanni Torrisi&Fabrizio Antonelli, Gianni Barlacchi,Marco De Nadai&Bruno Lepri, Northeastern University, Boston, Massachusetts 02115, USA, MIT Media Lab, Cambridge, Massachusetts 02139, USA, You can also search for this author in This dataset contains all the articles published on the website milanotoday.it from 01/11/2013 to 31/12/2013.The values are not spatially aggregated.The temporal aggregation values are discrete. 156 Recommendations 0 Learn more about stats on ResearchGate Abstract In this work, we are interested in the applications of big data in the telecommunication domain, analysing two weeks of. The current flowing through the distribution lines has been recorded every 10 minutes. This dataset provides information regarding the directional interaction strength between the city of Milan different areas based on the calls exchanged between Telecom Italia Mobile users. The second set is composed of the administrative boundaries used in the last three censuses. EPJ Data Science 4, 3 (2015). For this reason, we issued the data availability dataset which indicates whether the data has been collected or not for a specific time interval. CAS Abstract We apply spatio-temporal regression with partial differential equation regularization to the Telecom Italia mobile phone data. The possible values are, - 90: address (e.g., Via del Brennero, 52). Moreover, the emergence of new geo-located Information and Communications Technology (ICT) services like Twitter and Foursquare introduces further opportunities for researchers to inspect quantitatively different aspects of human behaviour such as the social well-being of individuals and communities19, socio-economic status of geographical regions20, and people's mobility21. Internet Explorer). It is a value between 0 and 3; Coverage: percentage value of the quadrant covered by the precipitation; Type: type of the precipitation. Not always available. Speed is in (m/s). For this reason, it is possible to more restrictively define hotspots using the Loubar threshold introduced in ref. Cell ID T imestamp Recevied SMS Activity Sent SMS Activity Incoming Calls Activity Outgoing Calls Activity. In the third layer we know how the customer sites of a power line are distributed over the grid and the energy flowing through each power-line (from the Line measurement dataset). Hawelka, B. et al. Because the 10 min interval dataset was quite sparse, it was not conducive to extracting spatiotemporal characteristics. 5) have a strong daily seasonal component which starts in the early morning and increases during the day, having a peak around 22:00. In Computational Approaches for Urban Environments 13, 363387 (2015). The data has been collected over two months, from November 1st, 2013 to January 1st, 2014 and the information is geo-referenced to the city of Milan and to the Province of Trentino. 100+ projects submitted. Miritello, G., Rubn, L., Cebrian, M. & Moro, E. Limited communication capacity unveils strategies for human interaction. master 1 branch 0 tags Go to file Code dwhitena Update README.md 398c34c on Apr 15, 2015 3 commits README.md Update README.md 8 years ago call_in_mgrid.png Initial Commit This dataset provides information regarding the directional interaction strength between the Province of Trento different areas based on the calls exchanged between Telecom Italia Mobile users. telecommunications street furniture poletop poles pole attachments + 23. Telecom Italia SpA fell on Tuesday following a Bloomberg report that Italy's state lender will drop its offer for the carrier's landline network, ending a bidding war with KKR & Co. Region: Europe and Central Asia This information is directly provided by ARPA (Agenzia Regionale per la Protezione dellAmbiente).Temporal aggregation 1 hour. Telecom Data Data Card Code (4) Discussion (1) About Dataset No description available Usability info License Unknown adult.data ( 3.97 MB) get_app fullscreen chevron_right Unable to show preview Unexpected end of JSON input Data Explorer Version 1 (5.98 MB) insert_drive_file adult.data insert_drive_file adult.test Summary arrow_right folder 2 files
arunasubbiah/milan-telecom-data-modeling - GitHub You can do anything you want, as you remain under the terms and conditions of the ODbL license conditions. During the same connection a CDR is generated if the connection lasts for more than 15min or the user transferred more than 5MB. Google Scholar, De Nadai, M. Harvard Dataverse https://doi.org/10.7910/DVN/UTLAHU (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/dvn/QJWLFU (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/dvn/FZRVSX (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/dvn/QLCABU (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/dvn/EGZHFV (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/dvn/F3RBMF (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/dvn/MAW5AR (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/dvn/KCRS61 (2015), Telecom Italia Harvard Dataverse https://doi.org/10.7910/dvn/JZMTBJ (2015), SpazioDati, DEIBPolitecnico di Milano. The data of Milan and Trentino are collected by ARPA (http://www.arpa.piemonte.it/rischinaturali) and by Meteotrentino (http://www.meteotrentino.it) respectively.
Does anyone know public open large datasets with data - ResearchGate Provided by the Springer Nature SharedIt content-sharing initiative, Scientific Data (Sci Data) McKinney, W. Data structures for statistical computing in Python. MobiHoc '15 Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 317326 (2015). We believe in the power of Open Data and we then decided to release them in Open Data. This process saves the author username, the tweet content and the time-stamp when the tweet has been written. This data was released under the Open Database License (ODbL) available in its raw form or through an API. The calls are issued from the nation identified by the Country code; Call-out activity: activity proportional to the amount of issued calls inside a given Square id during a given Time interval. However, he resolution of the data is not uniform over the national territory. From Figs 5 and 6 it is possible to observe a strong daily seasonality which usually starts at 7:00, when people turn on their phones and probably commute to work and then slowly decreases in the evening when people return home and sleep. The stream was gathered through the Twitter Streaming API (https://dev.twitter.com/docs/streaming-apis) which is a free service allowing the extraction of ~1% of the total Twitter feed through a set of filterers provided by the user. Alex Alley. This dataset provides information about the telecommunication activity over the city of Milano.The dataset is the result of a computation over the Call Detail Records (CDRs) generated by the Telecom Italia cellular network over the city of Milano. There is no spatial aggregation and the data are aggregated in timeslots of 15min. Some of the datasets referring to the Milan urban area are spatially aggregated using a grid. In the second layer we lose the exact geometries of customer sites and power lines. Grauwin, S., Sobolevsky, S., Moritz, S., Gdor, I. The precipitation types are described as: Absent: precipitation quantity equal to 0mm/h. This is achieved by treating the traffic volume data as a tensor, similar to an image, which is then fed to a convolutional neural network. 2). This dataset provides information about the telecommunication activity over the city of Milano. Use of any data must be accompanied by a hyperlink reading "from BigDataChallenge contest" and linking to either the ODI node Trento section homepage or the page referring to the information in question. Similarly to the physical network where people and goods move, the virtual network determines how information and knowledge moves. Dataset Telecom Italia organized the 'Telecom Italia Big Data Challenge' in 2014, they provided data of two Italian areas: the city of Milan and the Province of Trentino. Sci. The contest made available to developers, designers and scientists a large dataset of 30+ kinds of data (mobile, weather, energy, etc.)